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浙江大学学报（农业与生命科学版）
Journal of Zhejiang University (Agriculture and Life Sciences)  2019, Vol. 45 Issue (6): 760-766    DOI: 10.3785/j.issn.1008-9209.2019.01.111
Agricultural engineering     
Detection of capsaicin content by near-infrared spectroscopy combined with optimal wavelengths
Xiaohan Lü1(),Jinlin JIANG2,Jing YANG3,Jianying CHEN1,Haiyan CEN2,Hongfei FU1,Yifei ZHOU1
1.Hangzhou Academy of Agricultural Sciences, Hangzhou 310024, China
2.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
3.College of Agriculture and Food Science, Zhejiang A & F University, Hangzhou 311300, China
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Abstract  

In order to investigate the potential of near-infrared spectroscopy for accurately predicting the capsaicin content in fresh chili peppers, taking Hangzhou chili pepper as a material, the near-infrared spectroscopy was employed to acquire spectral information of chili peppers, and high-performance liquid chromatography was conducted to obtain the reference values of capsaicin content. Three different variable selection methods with successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformation variable elimination (UVE) were performed to select the optimal wavelengths. Partial least square (PLS) models based on full spectra and optimal wavelengths were then developed to predict the capsaicin content, and the prediction performances and operation efficiency were compared. The results showed that the CARS-PLS model yielded the best prediction performances, with the correlation coefficient of 0.838 6 and root-mean-square error of prediction set of 0.014 8 mg/g. In addition, compared with the full spectra of 200 wavelengths, the number of the optimal wavelengths selected by CARS was reduced by 96%, which indicated that optimal wavelengths can be used to simplify the models and improve the operation efficiency. The above results demonstrate that the near-infrared spectroscopy based on optimal wavelengths is feasible for the detection of capsaicin content.

Key words： near-infrared spectroscopy      capsaicin      chili pepper      optimal wavelengths     
Received: 11 January 2019      Published: 20 January 2020
CLC:  O 433.4  
Corresponding Authors: Xiaohan Lü     E-mail: 45718653@qq.com
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Xiaohan Lü
Jinlin JIANG
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Hongfei FU
Yifei ZHOU
Cite this article:

Xiaohan Lü,Jinlin JIANG,Jing YANG,Jianying CHEN,Haiyan CEN,Hongfei FU,Yifei ZHOU. Detection of capsaicin content by near-infrared spectroscopy combined with optimal wavelengths. Journal of Zhejiang University (Agriculture and Life Sciences), 2019, 45(6): 760-766.

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http://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2019.01.111     OR     http://www.zjujournals.com/agr/Y2019/V45/I6/760


基于特征波长建模的近红外光谱技术检测辣椒素含量

为实现近红外光谱技术对新鲜辣椒果实中辣椒素含量的准确预测，以杭椒类辣椒为研究对象，采集新鲜辣椒果实的近红外光谱信息，结合高效液相色谱法，分别采用连续投影算法（successive projection algorithm, SPA）、竞争性自适应重加权采样法（competitive adaptive reweighted sampling, CARS）、无信息变量消除法（uninformation variable elimination, UVE）提取特征波长，建立偏最小二乘法（partial least squares, PLS）预测模型，并比较了全谱建模与特征波长建模的预测效果和运算效率。结果显示：CARS-PLS模型的预测效果最好，预测集相关系数和均方根误差分别为0.838 6和0.014 8 mg/g。此外，与全谱建模的输入变量200相比，基于CARS选择的特征波长建模的输入变量减少了96%，这说明运用特征波长建模大大地简化了模型，提高了运算效率。本试验表明，基于特征波长建模的近红外光谱技术对于新鲜辣椒果实中辣椒素含量的检测是可行的。


关键词： 近红外光谱技术,  辣椒素,  辣椒,  特征波长 
Fig. 1 Frequency histogram of the capsaicin content of all the chili pepper samples
Fig. 2 Frequency histogram of the capsaicin content of chili pepper samples after removing abnormal samples

样本

Samples

最小值Minimum

value

最大值Maximum

value

平均值Mean

标准偏差Standard

deviation

建模集

Modeling set

0.000 40.118 60.033 50.029 9

预测集

Prediction set

0.000 20.122 90.030 70.027 3
Table 1 Capsaicin content of chili peppers for the modeling and prediction sets mg/g
Fig. 3 Average spectra of fresh chili peppers
Fig. 4 Predicted results of modeling set (A) and prediction set (B) of PLS model

方法

Method

个数

Number

特征波长

Optimal wavelength/nm

SPA61 146, 1 207, 1 281, 1 362, 1 511, 1 565
CARS81 143, 1 200, 1 274, 1 328, 1 382, 1 399, 1 436, 1 622
UVE81 140, 1 206, 1 277, 1 342, 1 402, 1 412, 1 470, 1 612
Table 2 Optimal wavelengths selected by three methods
Fig. 5 Predicted results of modeling set (A) and prediction set (B) of SPA-PLS model
Fig. 6 Predicted results of modeling set (A) and prediction set (B) of CARS-PLS model
Fig. 7 Predicted results of modeling set (A) and prediction set (B) of UVE-PLS model
[1]   LIU C, MA N, WANG P Y, et al. Transcriptome sequencing and de novo analysis of a cytoplasmic male sterile line and its near-isogenic restorer line in chili pepper (Capsicum annuum L.). PLoS One, 2013,8(6):e65209.
[2]   邹学校,张竹青,陈文超,等.辣椒果实性状的遗传分析.西北植物学报,2007,27(3):497-501.
ZOU X X, ZHANG Z Q, CHEN W C, et al. Genetic analyses of fruit characters in pepper (Capsicum annuum L.). Acta Botanica Boreali-Occidentalia Sinica, 2007,27(3):497-501. (in Chinese with English abstract)
[3]   刘辰,马宁,付楠,等.辣椒GMS育性相关候选基因的克隆及表达分析.中国农业科学,2014,47(16):3264-3276.
LIU C, MA N, FU N, et al. Cloning and expression analysis of fertility-related genes for the genic male sterile line in pepper (Capsicum annuum L.). Scientia Agricultura Sinica, 2014,47(16):3264-3276. (in Chinese with English abstract)
[4]   HONG S T, CHUNG J E, AN G, et al. Analysis of 176 expressed sequence tags generated from cDNA clones of hot pepper by single-pass sequencing. Journal of Plant Biology, 1998,41(2):116-124.
[5]   CHEN C M, HAO X F, CHEN G J, et al. Characterization of a new male sterility-related gene Camf1 in Capsicum annum L. Molecular Biology Reports, 2011,39(1):737-744.
[6]   KORKUTATA N F, KAVAZ A. A comparative study of ascorbic acid and capsaicinoid contents in red hot peppers (Capsium annum L.)grown in southeastern Anatolia Region. International Journal of Food Properties, 2015,18(4):725-734.
[7]   USMAN M G, RAFII M Y, LATIF M A, et al. Capsaicin and dihydrocapsaicin determination in chili pepper genotypes using ultra-fast liquid chromatography. Molecules, 2014,19(5):6474-6488.
[8]   WU S X, ZENG J M, XIE H, et al. Capsaicin determination and chili sauce discrimination using low-cost and portable electrochemical sensors based on all graphite pencil electrodes. Analytical Methods, 2016,8(39):7025-7029.
[9]   厉志伟,王盛莉.高效液相色谱法测定辣椒中辣椒素、二氢辣椒素.中国调味品,2017(11):127-130.
LI Z W, WANG S L. Determination of capsaicin and dihydrocapsaicin in capsicum by high performance liquid chromatography. China Condiment, 2017(11):127-130. (in Chinese with English abstract)
[10]   张晶,孙长波,石磊岭,等.RP-HPLC法测定辣椒中辣椒素、二氢辣椒素和降二氢辣椒素含量.药物分析杂志,2011,31(2):244-246.
ZHANG J, SUN C B, SHI L L, et al. RP-HPLC simul-taneous determination of three capsaicinoids constituent in fruits of Capsium annum L. Chinese Journal of Pharmaceutical Analysis, 2011,31(2):244-246. (in Chinese with English abstract)
[11]   刘菲,徐华,赵静,等.合成辣椒素新工艺.河南科学,2014,32(12):2471-2473.
LIU F, XU H, ZHAO J, et al. New process of synthetic capsaicin. Henan Science, 2014,32(12):2471-2473. (in Chinese with English abstract)
[12]   杨东顺,梅文泉,龙洪进,等.酶联免疫法测定辣椒中辣椒素含量.中国农学通报,2015,31(21):264-267.
YANG D S, MEI W Q, LONG H J, et al. Determination of capsaicin contents in Capsicum annuum L. samples by enzyme immunoassay. Chinese Agricultural Science Bulletin, 2015,31(21):264-267. (in Chinese with English abstract)
[13]   KOSUGE S, FURUTA M. Studies on the pungent principle of capsicum part ⅩⅣ chemical constitution of the pungent principle. Agricultural and Biological Chemistry, 1970,34(2):248-256.
[14]   THOMAS B V, SCHREIBER A A, WEISSKOPF C P. Simple method for quantitation of capsaicinoids in peppers using capillary gas chromatography. Journal of Agricultural and Food Chemistry, 1998,46(7):2655-2663.
[15]   马本学,肖文东,祁想想,等.基于漫反射高光谱成像技术的哈密瓜糖度无损检测研究.光谱学与光谱分析,2012,32(11):3093-3097.
MA B X, XIAO W D, QI X X, et al. Nondestructive measurement of sugar content of hami melon based on diffuse reflectance hyperspectral imaging technique. Spectroscopy and Spectral Analysis, 2012,32(11):3093-3097. (in Chinese with English abstract)
[16]   刘小丹,冯旭萍,刘飞,等.基于近红外高光谱成像技术鉴别杂交稻品系.农业工程学报，2017,33(22):189-194.
LIU X D, FENG X P, LIU F, et al. Identification of hybrid rice strain based on near-infrared hyperspectral imaging technology. Transactions of the CSAE, 2017,33(22):189-194. (in Chinese with English abstract)
[17]   LEIVA V G A, LU R F, AGUILERA J M, et al. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering, 2013,115(1):91-98.
[18]   牛晓颖,周玉宏,邵利敏.基于LS-SVM的草莓固酸比和可滴定酸近红外光谱定量模型.农业工程学报,2013,29(增刊1):270-274.
NIU X Y, ZHOU Y H, SHAO L M. Improved NIR quantitative model of soluble solids titratable acid ratio and titratable acidity in strawberry based on LS-SVM. Transactions of the CSAE, 2013,29(Suppl. 1):270-274. (in Chinese with English abstract)
[19]   中华人民共和国农业部.辣椒素的测定 高效液相色谱法:NY/T 1381—2007.[S.l.: s.n.],2007.
Ministry of Agriculture of the People’s Republic of China. Determination of capsaicin by high performance liquid chromatography: NY/T 1381—2007. [S. l. s. n.], 2007.
[20]   田海清,应义斌,徐惠荣.西瓜可溶性固形物含量近红外透射检测技术.农业机械学报,2007,38(5):111-113.
TIAN H Q, YING Y B, XU H R. Near-infrared transmittance measuring technique for soluble solids content of watermelon. Transactions of the Chinese Society for Agricultural Machinery, 2007,38(5):111-113. (in Chinese with English abstract)
[21]   LIU F, JIANG Y H, HE Y. Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: a case study to determine soluble solids content of beer. Analytica Chimica Acta, 2009,635(1):45-52.
[22]   ARAUJO M C U, SALDANHA T C B, GALVAO R K H, et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemo-metrics and Intelligent Laboratory Systems, 2001,57(2):65-73.
[23]   ZHANG J K, RIVARD B, ROGGE D M. The successive projection algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data. Sensors, 2008,8(2):1321-1342.
[24]   LI H D, LIANG Y Z, XU Q S, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 2009,648(1):77-84.
[25]   YUN Y H, WANG W T, DENG B C, et al. Using variable combination population analysis for variable selection in multivariate calibration. Analytica Chimica Acta, 2015,862:14-23.
[26]   CENTNER V, MASSART D L, DE NOORD O E. Elimination of uninformative variables for multivariate calibration. Analytical Chemistry, 1996,68(21):3851-3858.
[27]   TEOFILO R F, MARTINS J P A, FERREIRA M M C. Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression. Journal of Chemometrics, 2009,23(1):32-48.
[28]   ALESSANDRINI L, ROMANI S, PINNAVAIA G, et al. Near infrared spectroscopy: an analytical tool to predict coffee roasting degree. Analytica Chimica Acta, 2008,625(1):95-102.
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